PAPILA dataset: a regional emission inventory of reactive gases for South America based on the combination of local and global information

Abstract. The multidisciplinary project Prediction of Air Pollution in Latin America and the Caribbean (PAPILA) is dedicated to the development and implementation of an air quality analysis and forecasting system to assess pollution impacts on human health and economy. In this context, a comprehensive emission inventory for South America was developed on the basis of the existing data on the global dataset CAMS-GLOB-ANT v4.1 (developed by joining CEDS trends and EDGARv4.3.2 historical data), enriching it with derived data from locally available emission inventories for Argentina, Chile and Colombia. This work presents the results of the first joint effort of South American researchers and European colleagues to generate regional maps of emissions, together with a methodological approach to continue incorporating information into future versions of the dataset. This version of the PAPILA dataset includes CO, NOx, NMVOCs, NH3 and SO2 annual emissions from anthropogenic sources for the period 2014–2016, with a spatial resolution of 0.1° x 0.1° over a domain that covers 32°–120° W and 34°N–58°S. PAPILA dataset is presented as netCDF4 files and is available in an open access data repository under a CC-BY 4 license: http://dx.doi.org/10.17632/btf2mz4fhf.2 . A comparative assessment of PAPILA-CAMS datasets was carried out for (i) the South American region, (ii) the countries with local data (Argentina, Colombia and Chile), and (iii) downscaled emission maps for urban domains with different environmental and anthropogenic factors. Relevant differences were obtained both at country and urban level for all the compounds analysed. Among them, we found that when comparing total emissions of PAPILA versus CAMS datasets at the national level, higher levels of NOx and considerably lower of the other species were obtained for Argentina, higher levels of SO2 and lower of CO and NOx for Colombia, and considerably higher levels CO, NMVOCs and SO2 for Chile. These discrepancies are mainly related to the representativeness of the local practices in the local emissions estimates, to the improvements made in the spatial distribution of the locally estimated emissions, or both. Both datasets were evaluated relative to surface concentrations of CO and NOx by using them as input data to the WRF-Chem model for one of the analysed domains, the Metropolitan Area of Buenos Aires, for summer and winter of 2015. For winter, PAPILA-based results had lower bias for CO and NOx concentrations, for which CAMS-based results tended to be underestimated. Both inventories exhibited similar performances for CO in summer, while PAPILA simulation outperformed NOx concentrations. These results highlight the importance of refining global inventories with local data to obtain accurate results with high-resolution air quality models.